DocumentCode
2947291
Title
Effects of norms on learning properties of support vector machines
Author
Ikeda, Kazushi ; Murata, Noboru
Author_Institution
Graduate Sch. of Informatics, Kyoto Univ., Japan
Volume
5
fYear
2005
fDate
18-23 March 2005
Abstract
Support vector machines (SVMs) are known to have a high generalization ability, yet a heavy computational load since margin maximization results in a quadratic programming problem. It is known that this maximization task results in a pth-order programming problem if we employ the LP norm instead of the L2 norm. In this paper, we theoretically show the effects of p on the learning properties of SVMs by clarifying its geometrical meaning.
Keywords
generalisation (artificial intelligence); learning (artificial intelligence); quadratic programming; support vector machines; L2 norm; LP norm; SVM; computational load; generalization ability; geometrical meaning; learning properties; margin maximization; norm effects; pth-order programming problem; quadratic programming problem; support vector machines; Computer errors; Computer simulation; Government; Informatics; Linear programming; Linearity; Machine learning; Quadratic programming; Signal processing; Support vector machines;
fLanguage
English
Publisher
ieee
Conference_Titel
Acoustics, Speech, and Signal Processing, 2005. Proceedings. (ICASSP '05). IEEE International Conference on
ISSN
1520-6149
Print_ISBN
0-7803-8874-7
Type
conf
DOI
10.1109/ICASSP.2005.1416285
Filename
1416285
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